Generating active links between model objects

ABSTRACT

Embodiments relate to generating active links between model objects. A modeling client can host modeling logic and an application programming interface (API) to create, access, manipulate, and import/export modeling objects used in modeling applications, such as engineering, medical, financial, and other modeling platforms. The source data accepted into the modeling client can include consumer or business-level applications, whose spreadsheet, database or other content can be extracted and encapsulated in object-oriented format, such as extensible markup language (XML) format. Links can be inserted in the resulting model object to link to external resources, such as additional model objects, services, local or remote modeling tools, or other resources. The model object can share, exchange, or combine data from other model object(s), as well as instantiate functions hosted in other model object(s). Multiple links can be inserted to multiple model objects in linked list, node, or other configurations.

FIELD

The present teachings relate to systems and methods for generatingactive links between model objects, and more particularly to platformsand techniques for dedicated modeling of technical, medical, financial,and other systems which are configured to generate model objects andactive linkages between those model objects to share data, functions,and other attributes.

BACKGROUND OF RELATED ART

A spectrum of modeling platforms and options exist today for engineers,managers, developers and other professionals. In the case ofengineering, medical, technical, financial, and other advanced modelingresources, a range of platforms are available for users interested insetting up, running and maintaining financial modeling systems. Forexample, organizations interested in relatively sophisticated modelingapplications, such as geophysical models for detecting oil reserves orother geologic features or equity market analysis based on Black-Sholesoption pricing models, a company or other organization may choose toinstall advanced modeling software on mainframe-class computers to runthose classes of models and obtain various projections, reports, andother results. Such mainframe platform, data center and relatedinstallations, however, can involve costs on the order of millions ofdollars or more, and may require the full time attention of highlyskilled professionals, including programmers and managers with advancedtraining. As a consequence, putting a mainframe-based modeling operationinto place may not be practical or possible for many organizations orusers.

On the other end of the spectrum, managers, engineers and others mayemploy widely available entry-level applications to capture operationaldata and attempt to develop predictive models for engineering,financial, medial, and other applications. That class of applicationscan include, for example, consumer or business-level spreadsheet,database, or data visualization programs for technical, financial, andother purposes. For instance, a manager of a manufacturing facility mayuse a commercially available spreadsheet application to enter productionnumbers, schedules, and other details of that site. However, attemptingto extract useful modeling outputs from those classes of applicationscan be difficult or impossible. For one, spreadsheet, database, andother widely available applications are typically built to producereports based on already existing data, but not to generate modelingoutputs or objects that represent predictive outputs or scenarios. Foranother, existing spreadsheet, database, and other applicationstypically involve limitations on cell size, number of dimensions,overall storage capacity, and other program parameters which, in thecase of large-scale modeling operations, may be insufficient to operateon the data sets necessary to produce and run meaningful models.

For another, the data structures and outputs of existing spreadsheet,database and other entry-level or commonly available applications aretypically arranged in proprietary format, rather than a widelyinteroperable object-based or other universal format. As still anotherdrawback, the cells, rows, columns, and other data elements withincommonly available spreadsheets, databases, and other entry-levelprograms can not be extracted as separate units and exported to othermodeling or analytic tools. In short, the use of spreadsheet, database,and other consumer or business-level applications to conduct modelingoperations involves significant shortcomings, due in part to the factthat those classes of platforms are not designed to reliable handlemodeling functionality. At present, therefore, a manager, developer,engineer, or other professional or user with modeling requirements isfaced with a choice between installing a large and expensivemainframe-based solution with its attendant infrastructure, aspreadsheet or database-based entry level solution with its attendantlimitations on power and data handling, or a combination of those twotypes of platforms. It may be desirable to provide object-based orobject-compatible modeling platforms capable of generating modelingobjects which encapsulate various modeling features, and whose contentis linkable via active links to exchange or share data, functions, orother attributes.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the presentteachings and together with the description, serve to explain theprinciples of the present teachings. In the figures:

FIG. 1 illustrates an overall system for a modeling network includingvarious hardware and connectivity resources that can be used in systemsand methods for generating active links between model objects, accordingto various embodiments of the present teachings;

FIG. 2 illustrates an exemplary modeling network including a modelingserver and connectivity resources, according to various embodiments;

FIG. 3 illustrates an exemplary hardware configuration for a modelingserver that can be used in systems and methods for generating activelinks between model objects, according to various embodiments;

FIG. 4 illustrates a flow diagram of overall modeling processing forobject-based modeling that can be used in systems and methods forgenerating active links between model objects, according to variousembodiments;

FIG. 5 illustrates exemplary operations to generate modeling objectsthat can contain links to other objects or other external resources,according to various embodiments; and

FIG. 6 illustrates a flow diagram of processing to generate modelingmodel objects having links to other objects or other external resources,according to various embodiments.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present teachings relate to systems and methods forgenerating active links between model objects. More particularly,embodiments relate to platforms and techniques that can access, extract,and generate modeling objects in a native object-based orobject-compatible format. The modeling objects produced via a modelingclient or other modeling tool according to the present teachings canencapsulate both source data describing a physical, medical, technical,financial, or other process or phenomena, and modeling attributes thatrelate the source data to predictive scenarios, specific models, andother features. In embodiments, the modeling objects can be extracted or“lifted” from data sources such as database programs or others, andstored to local storage of a local modeling client. The model objectscan include links to resources that are external to the respectiveobjects, including other model objects. The linked model objects canthereby access, share, and exchange data elements as well as functions,procedures, and other processes or services. The linked model objectscan be made visible or available via the desktop or other user interfaceof the modeling client. These and other embodiments described hereinaddress the various noted shortcomings in known modeling technology, andprovide a user or operator with enhanced modeling power on a desktop orother client, allowing direct extraction and manipulation of databasedimensions as independent object-based entities. Systems and methodsaccording to the present teachings also allowing seamless generation,local storage, and communication of model objects to backend mainframeplatforms, data centers, middleware servers, other modeling clients,and/or other local or remote modeling, storage, or data processingresources.

Reference will now be made in detail to exemplary embodiments of thepresent teachings, which are illustrated in the accompanying drawings.Where possible the same reference numbers will be used throughout thedrawings to refer to the same or like parts.

FIG. 1 illustrates an overall network 100 in which systems and methodsfor generating active links between model objects can be implemented,consistent with various embodiments of the present teachings. Inembodiments as shown, a modeling client 102 can communicate with avariety of local and remote resources, including an mainframe platform202 via one or more network 112. Client 102 can be or include, forinstance, a personal computer, a server, a dedicated workstation, amobile device, or other machine, device, hardware, or resource. One ormore network 112 can be or include, for example, the Internet, a virtualprivate network (VPN), a local area network such as an Ethernet network,or other public or private network or networks. Mainframe platform 202can be or include commercially available platforms or installations,such as, merely for example, mainframe or enterprise platforms availablefrom SAP Inc. of Walldorf, Germany, and other sources.

Mainframe platform 202 can include modules, logic, and functionality toperform an array of computation and data storage tasks, including datawarehousing, data mining, statistical analyses, financial planning,inventory management, customer resource management, engineering design,and other applications. In implementations as shown, mainframe platform202 can host or communicate with a variety or resources including,merely illustratively, a mainframe data store 206, and logic orapplications including an analytic module 204. Mainframe platform 202can contain, host, support, or interface to other data processinghardware, software, and other resources. In embodiments, modeling client102 can likewise communicate with other local or remote resources, suchas a middleware server 208 hosting or interfacing to a set of datastores for online analytical processing (OLAP) or other functions.Modeling client 102 can also communicate or interface with other localor remote servers, services, data stores, or other resources.

In embodiments as shown, modeling client 102 can operate under anoperating system 118, such as a distribution of the LInuX™, Unix™, orother open source or proprietary operating system. Modeling client 102can present a user interface 130, such as a graphical user interface orcommand line interface, operating under operating system 118 to receivecommands and inputs from a user, and operate modeling client 102.Modeling client 102 can communicate with storage resources including amodeling store 104, such as a local or remote database or data store.Modeling store 104 can store a set of modeling objects 106, in whichdata, functions, procedures, attributes, and/or other informationrelated to one or more modeling object 110 can be encapsulated andstored. In embodiments, modeling object 110 can be encoded in extensiblemarkup language (XML) format. In embodiments, modeling object 110 can beencoded in other object-based or object-compatible formats or datastructures. Modeling client 102 can communicate with mainframe platform102 via a modeling application programming interface (API) 108. Modelingapplication programming interface (API) 108 can include, for instance,defined function calls or calls to other routines, calculations, orfeatures, as well as data structures and parameters associated withmodeling operations. For example, modeling application programminginterface (API) 108 can include a function call to invoke a Monte Carlosimulation model based on a set of supplied data, such as an identifiedset of dimensions extracted from a spreadsheet or database. Otherfunctions, routines, resources, and features can be called, invoked, orinstantiated via modeling application programming interface (API) 108.According to embodiments in various regards, one or more local or remotemodeling packages, modules, or other supporting applications can beinstantiated via modeling module 120 and modeling applicationprogramming interface (API) 108 to manipulate source data and resultingone or more modeling object 110.

In embodiments, a user of modeling client 102 can access, modify, or adddata modeling objects to a set of data modeling object 106 via amodeling module 120 hosted in modeling client 102. Set of data modelingobjects 106 can include data objects that the user of modeling client102 has directly entered, or, in aspects, which the user of modelingclient has imported or extracted from sources such as consumer orbusiness-level spreadsheet, database, and/or other applications orplatforms. Modeling module 120 can itself be or include applications,software modules or hardware modules, or other logic or resources tooperate on set of modeling objects 106. Modeling module 120 can, merelyillustratively, include or access logic or modules for invoking andmanipulating a variety of scientific, technical, engineering, medical,financial, manufacturing, or other modeling operations. For instance,modeling module 120 can be or include applications or logic forperforming Monte Carlo simulations, finite element analyses,Black-Scholes option pricing or other market analyses, epidemiologicalprojections, geophysical models or simulations, or other simulations,models, trend mappings, projections, or other predictive processes. Inembodiments in one regard, after invoking modeling module 120 andperforming any modeling task, the user of modeling client 102 canlocally store and/or export one or more modeling object 110 to externalplatforms or resources.

In embodiments as shown, the user of modeling client 102 can forinstance export or communicate one or more modeling object 110 tomainframe platform 202 via modeling application programming interface(API) 108, for storage and use at a local or remote location from withinthat platform. In aspects, mainframe platform 202 can receive modelingobject 110 directly, without a necessity for translation, re-formatting,or invoking any spreadsheet, database, or other application from whichdata encapsulated in one or mode modeling object 110 originated. Inaspects, mainframe platform 202 can operate on one or more modelingobject 110, and transmit or return that data or other results tomodeling client 102 via modeling application programming interface (API)108. Thus, according to aspects of the present teachings, modelingobjects can be exchanged directly and programmatically between modelingclient 102, mainframe platform 202 or other larger-scale or remoteplatforms, including for instance middleware server 208 or othercomparatively large-scale or higher-capacity modeling or analytic tools.

In terms of operating on source data and generating one or more modelingobject 110 for local storage and/or exchange with mainframe platform 202or other platforms, and as shown for instance in FIG. 2, according tovarious embodiments, a user of modeling client 102 can invoke modelingmodule 120 to manipulate a set of source data 114 to identify,configure, and/or extract the functional objects, attributes, or otherfeatures of a set of data to produce a modeling output. In embodimentsas shown, modeling module 120 can access a set of source data 114, fromwhich data, attributes, and/or other metadata can be extracted togenerate one or more modeling object 110. In aspects, set of source data114 can be generated, hosted, or stored by or in a local application134, such as a spreadsheet, database, accounting, word processing,presentation, or other application or software. In aspects, set ofsource data 114 can comprise data previously or newly generated in theform of an object-based modeling object, such as a modeling objectentered, imported, or specified by the user of modeling client 102. Inaspects, set of source data 114 can comprise data originally stored orgenerated in a consumer or business-level spreadsheet, database, and/orother application or software. In aspects, set of source data 114 can beinitially formatted or encoded in a non-object oriented format, such asin a cellular array or in a relational database format. In aspects, setof source data 114 can be initially formatted or encoded in anobject-oriented format, such as extensible markup language (XML) format.In aspects, a user of modeling client 102 can highlight, select, orotherwise specify all or a portion of set of source data 114 to generateone or more extracted functional object 116. For instance, a user canhighlight a column of set of source data 114 to identify and extractdata as well as functional relationships of interest, to the user, as aunified object. Thus, purely illustratively and as shown, a user maywish to obtain a view on a current month's sales figures including grosssales, tax, production or delivery costs, and cost basis, as well asother parameters related to sales activity. In aspects as shown, a usercan, for instance, highlight those functional relationships by dragginga cursor or otherwise selecting a set of cells to group together, andform one or more extracted functional object 116. In aspects, selectioncan include the extraction of set of data elements 136, such as valuesstored in spreadsheet cells or database entries. In aspects, once a setof data elements 136 are selected, the functional, computational, orother modeling parameters associated with that data can be stored orassociated with one or more extracted functional object 116. Forinstance, modeling module 120 can store associated routines,computations, processes, or other attributes or functionalspecifications for one or more extracted functional object 116 in set ofattributes 122, which can be stored or associated with one or moreextracted functional object 116. In aspects, set of attributes 122 caninclude the identification of or linkage to any routines, interfaces, orother functional or computational resources that will be associated withone or more extracted functional object. According to variousembodiments, analytic module 204 of mainframe platform 202, or otherresource or platform receiving one or more extracted functional object116 from modeling client 102 can thereby obtain both data values derivedor obtained from set of source data 114, as well as functional orprocedural resources and relationships associated with that data. One ormore extracted functional object 116 along with any associated set ofattributes 122 can be encoded or stored in one or more modeling object110, which can thereby be transparently exported to mainframe platform202, middleware server 208, or other platforms or destinations forfurther modeling operations.

FIG. 3 illustrates an exemplary diagram of hardware, software,connectivity, and other resources that can be incorporated in a modelingclient 102 configured to communicate with one or more network 112,including to interface with mainframe platform 202, middleware server208, and/or other local or remote resources. In embodiments as shown,modeling client 102 can comprise a processor 124 communicating withmemory 126, such as electronic random access memory, operating undercontrol of or in conjunction with operating system 118. Operating system118 can be, for example, a distribution of the Linux™ operating system,the Unix™ operating system, or other open-source or proprietaryoperating system or platform. Processor 124 also communicates with amodel store 104, such as a database stored on a local hard drive, whichmay store or host set of modeling objects 106. Processor 124 furthercommunicates with network interface 128, such as an Ethernet or wirelessdata connection, which in turn communicates with one or more networks112, such as the Internet, or other public or private networks.Processor 124 also communicates with modeling module 120 along withmodeling application programming interface (API) 108 and/or otherresources or logic, to execute control and perform modeling calculation,translation, data exchange, and other processes described herein. Otherconfigurations of the network modeling client 102, associated networkconnections, and other hardware and software resources are possible.While FIG. 3 illustrates modeling client 102 as a standalone systemcomprises a combination of hardware and software, modeling client 102can also be implemented as a software application or program capable ofbeing executed by a conventional computer platform. Likewise, modelingclient 102 can also be implemented as a software module or programmodule capable of being incorporated in other software applications andprograms. In either case, modeling client 102 can be implemented in anytype of conventional proprietary or open-source computer language.

FIG. 4 illustrates a flow diagram of overall processing that can be usedin systems and methods for generating active links between modelobjects, according to various embodiments. In 402, processing can begin.In 404, a user of modeling client 102 or other client or device caninvoke or instantiate modeling module 120 or other logic, to performmodeling operations. In 406, modeling module 120 can access model store104 and extract one or more modeling object 110 from set of modelingobjects 106. In 408, modeling computations or other operations can beperformed on one or more modeling object 110. For example, a modelingoperation can be performed to project or predict the output of a factorybased on various supply scenarios for parts, materials, energy costs, orother variables. In 410, the values, functions, linkages, or otherattributes of one or more data modeling object 110 that were accessed,produced, or modified by the modeling operations can be captured, fixed,or locked down by modeling module 120. For instance, the resulting oneor more modeling object 110 can be stored to set of modeling objects 106in model store 104, or other databases or data stores.

In 412, modeling application programming interface (API) 108 can beinvoked by modeling module 120, by mainframe platform 202, or otherresources to transfer one or mode modeling object 110 to mainframeplatform 202. In embodiments, one or more modeling object 110 can forinstance be communicated to mainframe platform 202 via a secureconnection or channel, such as a secure socket layer (SSL) connection,via a channel encrypted using a public/private key infrastructure, orother channel or connection. In 414, one or more model object 110 can bereceived in modeling module 120 from mainframe platform 202 or otherresource, as appropriate. For example, an updated version of one or moremodel object 110 reflecting new data, new modeling results, or otherinformation can be received in modeling module 120. In 416, theresulting new, updated, or modified one or more model object 110 can bestored to set of modeling objects 106 in model store 104, asappropriate. In embodiments, one or more model objects 110 can inaddition or instead be stored to mainframe data store 206, to middlewareserver 208, to another modeling client or other client, or other site ordestination. in 418, modeling module 120 can convert one or more modelobjects 110 to spreadsheet, database, or other format, and export anyconverted data as a set of cell-formatted information, or data encodedin other formats. For instance, modeling module 120 can convert ortranslate one or more model objects to cell data values or databaseentries, and export that data to client-level applications on modelingclient 102 or other local or remote devices or storage. In 420,processing can repeat, return to a prior processing point, jump to afurther processing point, or end.

According to various embodiments of the present teachings, and as forexample generally illustrated in FIG. 5, in implementations modelingmodule 120 can generate modeling objects by operating on one or more setof source data 114, to create a model object and a second model object132, for instance using techniques described herein. In aspects, modelobject 110 can include a set of external resource links 138 external tothe object, such as hyperlinks or other links, pointers, or identifiers.In embodiments, each link in set of external resource links 138 canspecify or identify one or more resources to which a model object, suchas model object 110, can link, connect, communicate, or otherwiseinteract, including other one or more additional model objects, datafields or functions in those additional model objects, services, Websites, applications, and/or other resources. In embodiments, modelingmodule 120 can insert set of external resource links 138 as informationin set of attributes 122 associated with model object 110, second modelobject 132, and/or other objects or entities.

In embodiments, the linkage identified in set of external resource links138 can link to second model object 132, and for instance specify that adata element located in a certain location of second model object 132 beautomatically imported into a corresponding cell, field, or other siteof model object 110, for instance whenever the data element populatingsecond model object 132 at that site is updated. In embodiments,functions or other procedures or calls can be imported from second modelobject 132 to model object 110. In embodiments, set of external resourcelinks 138 can contain links or mappings from one data element orfunction to multiple data fields, elements or functions of differentmodel objects, from multiple data fields, elements or function tomultiple data elements or functions, from multiple data fields, elementsor functions to a single data element or function, or otherconfigurations. In embodiments, two or more model objects can be linkedvia set of external resource links 138 in a chain, tree, node, web, orother configurations.

In embodiments in various regards, once model object 110, second modelobject 132, and any other objects are extracted, modeling module 120 cancapture and store, or lock down, the data elements or content of thoseobjects along with any attributes or other metadata, including set ofexternal resource links 138, in or in association with each modelingobject. In embodiments, model object 110, second model object 132, andothers so generated can be stored in model store 104, or other localstorage of modeling client 102. In embodiments as shown, therefore,model object 110 and second model object 132 can reside on modelingclient 102, and any one or more of the resources linked between thoseobjects or other resources can be seamlessly shared on modeling client102. It may be noted that while each of model object 110 and secondmodel object 132 can be stored or hosted in modeling client 102, inembodiments, either of model object 110, second model object 132, andother objects can also or instead be hosted or stored in other local orremote platforms, such as mainframe platform 202, middleware servers,other modeling or other clients, or other destinations.

FIG. 6 illustrates a flow diagram of overall processing to generate andmanipulate one or more model object 110 having links to additional modelobjects or other external resources, according to various embodiments.In 602, processing can begin, In 604, a user can invoke or initiatemodeling module 120 and/or local application 134, such as, for instance,a spreadsheet application, a database application, or other applicationsor software. In 606, the user can select data in set of source data 114or other data sources to generate a model object 110, including relateddata elements and attributes. In 608, the user can select data in set ofsource data 114 or other data sources to generate second model object132, including related data elements and attributes for that object.

In 610, the user can insert a set of external resource links 138 ineither of model object 110 and/or second model object 132, for instance,by highlighting desired data elements, functions, or other informationin each object, and selecting a linkage type (e.g., hyperlink, pointer,one-way link, two-way link, or other linkage) or other mapping betweenthe objects. In aspects, multiple linkages between elements or functionsof either model object 110 and/or second model object 132 can beestablished. In 612, modeling module 120 can store model object 110and/or second model object 132 to local storage of modeling client 102,such as model store 104 or other local storage, as appropriate. In 614,modeling module 120 can access model object 110, and decode set ofexternal resource links 138 to identify second model object 132 or otherexternal resources to associate and link with model object 110. In 616,modeling module 120 can invoke and import, receive, update, or otherwiseprocess the data, functions, or other resources so linked to modelobject 110, such as, for instance, to automatically import any updateddata from certain sections of second model object 132 (e.g., columns 2and 3), or call a function or routine specified by a cell (e.g., cell)of second model object 132. In 618, any updates to model object 110 canbe stored to model store 104 or other storage, as appropriate. In 620,processing can repeat, return to a prior processing point, jump to afurther processing point, or end.

The foregoing description is illustrative, and variations inconfiguration and implementation may occur to persons skilled in theart. For example, while embodiments have been described wherein one ormore model object 110 is accessed and manipulated via one modelingclient 102, in embodiments, one or more users can use multiple modelingclients, or networks including modeling clients or other resources, tooperate on model object data. For further example, while embodimentshave been described in which modeling client 102 may interact with onemainframe platform 202 and/or one middleware server 208, in embodiments,one or more modeling client 102 can interact with multiple mainframeplatforms, data centers, middleware servers, and/or other resources, invarious combinations. Yet further, while embodiments have been describedin which a modeling client 102 interacts with a mainframe platform 202and/or middleware server 208, in embodiments, rather than interact withlarge-scale mainframe platforms, data centers, or middleware servers,modeling client 102 can interact with other local or remote modelingclients, networks of those clients, or, in embodiments, can operate toperform modeling operations on a stand-alone basis, without necessarilycommunicating with other modeling platforms. Other resources describedas singular or integrated can in embodiments be plural or distributed,and resources described as multiple or distributed can in embodiments becombined. The scope of the present teachings is accordingly intended tobe limited only by the following claims.

What is claimed is:
 1. A method comprising: invoking an application, theapplication comprising source data, wherein the source data comprises acellular array format; encapsulating a first set of cells of the sourcedata in a first predictive model object, wherein a modeling applicationperforms an operation on the first predictive model object to predict anoutput, and wherein the first predictive model object is encoded inextensible markup language (XML) format; encapsulating a second set ofcells of the source data in a second predictive model object, whereinthe second predictive model object is encoded in XML format and, a cellof the second set of cells in the second predictive model objectspecifies a function; receiving a user selection of a linkage type foran import link, wherein the import link imports the function specifiedby the cell of the second set of cells in the second predictive modelobject as a corresponding function in a corresponding cell in the firstset of cells in the first predictive model object; inserting the importlink in the first predictive model object in view of the user selectionof the linkage type; changing, by the modeling application, the functionspecified by the cell of the second set of cells in the secondpredictive model by performing the operation on the second predictivemodel object to predict a second output; and calling, by a clientdevice, the changed function specified by the cell of the second set ofcells in the second predictive model object as the correspondingfunction in the corresponding cell in the first set of cells in thefirst predictive model object using the import link in response to thefunction in the second predictive model being changed.
 2. The method ofclaim 1, further comprising: storing the first predictive model objectand the second predictive model object in storage of the client device.3. The method of claim 1, wherein the first predictive model object andthe second predictive model object are linked in at least one of a tree,node, or web configuration.
 4. The method of claim 1, wherein the firstpredictive model object and the second predictive model object comprisean attribute, the attribute comprising at least one of a function callparameter or a linkage parameter.
 5. The method of claim 1, wherein thelink comprises a plurality of links.
 6. The method of claim 5, whereinthe plurality of links invoke at least one of a plurality of additionalpredictive model objects, a mainframe modeling platform, or anadditional modeling client device.
 7. The method of claim 1, wherein thesource data comprises at least one of spreadsheet data, database data,word processing data, or presentation data.
 8. The method of claim 1,where the source data is hosted in at least one of local storage of theclient device or remote storage.
 9. The method of claim 1, wherein theoperation is a Monte Carlo operation.
 10. A client system comprising: aninterface to source data in an application; and a processor,communicating with the source data via the interface, the processor to:invoke an application, the application comprising source data, whereinthe source data comprises a cellular array format, encapsulate a firstset of cells of the source data in a first predictive model object,wherein a modeling application performs an operation on the firstpredictive model object to predict an output, and wherein the firstpredictive model object is encoded in extensible markup language (XML)format, encapsulate a second set of cells of the source data in a secondpredictive model object, wherein the second predictive model object isencoded in XML format and, a cell of the second set of cells in thesecond predictive model object specifies a function, receive a userselection of a linkage type for an import link, wherein the import linkimports the function specified by the cell of the second set of cells inthe second predictive model object as a corresponding function in acorresponding cell in the first set of cells in the first predictivemodel object; insert the import link in the first predictive modelobject in view of the user selection of the linkage type, change, by themodeling application, the function specified by the cell of the secondset of cells in the second predictive model by performing the operationon the second predictive model object, and call the changed functionspecified by the cell of the second set of cells in the secondpredictive model object as the corresponding function in thecorresponding cell in the first set of cells in the first predictivemodel object sing the import link in response to the function in thesecond predictive model being changed.
 11. The system of claim 10,further comprising: a memory coupled to the processor to store the firstpredictive model object and the second predictive model object.
 12. Thesystem of claim 10, wherein the first predictive model object and thesecond predictive model object are linked in at least one of a tree,node, or web configuration.
 13. The system of claim 10, wherein thefirst predictive model object and the second predictive model objectcomprise an attribute, the attribute comprising at least one of afunction call parameter or a linkage parameter.
 14. The system of claim10, wherein the link comprises a plurality of links.
 15. The system ofclaim 14, wherein the plurality of links invoke at least one of aplurality of additional predictive model objects, a mainframe modelingplatform, or an additional modeling client system.
 16. The system ofclaim 10, wherein the source data comprises at least one of spreadsheetdata, database data, word processing data, or presentation data.
 17. Thesystem of claim 10, where the source data is hosted in at least one oflocal storage of the client system or remote storage.
 18. The clientsystem of claim 10, wherein the operation is a Monte Carlo operation.19. A non-transitory computer system readable medium includinginstructions that, when executed by a processor, cause the processor toperform operations comprising: invoking an application, the applicationcomprising source data, wherein the source data comprises a cellulararray format; encapsulating a first set of cells of the source data in afirst predictive model object, wherein a modeling application performsan operation on the first predictive model object to predict an output,and wherein the first predictive model object is encoded in extensiblemarkup language (XML) format; encapsulating a second set of cells of thesource data in a second predictive model object, wherein the secondpredictive model object is encoded in XML format and, a cell of thesecond set of cells in the second predictive model object specifies afunction; receiving a user selection of a linkage type for an importlink, wherein the import link imports the function specified by the cellof the second set of cells in the second predictive model object as acorresponding function in a corresponding cell in the first set of cellsin the first predictive model object; inserting the import link in thefirst predictive model object in view of the user selection of thelinkage type; changing, by the modeling application, the functionspecified by the cell of the second set of cells in the secondpredictive model by performing the operation on the second predictivemodel object to predict a second output; and calling, by the processor,the changed function specified by the cell of the second set of cells inthe second predictive model object as the corresponding function in thecorresponding cell in the first set of cells in the first predictivemodel object using the import link in response to the function in thesecond predictive model being changed.
 20. The non-transitory computersystem readable medium of claim 19, wherein the first predictive modelobject and the second predictive model object are linked in at least oneof a tree, node, or web configuration.
 21. The non-transitory computersystem readable medium of claim 19, wherein the first attribute and thesecond attribute comprise at least one of a function call parameter or alinkage parameter.
 22. The non-transitory computer system readablemedium of claim 19, wherein the link comprises a plurality of links toinvoke at least one of a plurality of additional predictive modelobjects, a mainframe modeling platform, or an additional modeling clientdevice.
 23. The non-transitory computer system readable medium of claimof claim 19, wherein the operation is a Monte Carlo operation.